AbstractThis paper presents a sequential optimum design approach for estimating the parameters of an atmospheric dispersion process model based on measurement data gathered by a team of cooperating sensor-equipped UAVs. Locally optimal waypoint sequences that account for each UAV’s possibly heterogeneous motion dynamics are computed by minimizing a suitable optimality criterion. Following these waypoints, the UAVs cooperatively maximize the information gain of the acquired measurements. A decentralized data-driven online control scheme is proposed that couples parameter estimation, waypoint calculation, and vehicle control and enables the UAVs to adaptively observe the dynamic process and iteratively improve the parameter estimate. Simulations demonstrate the effectiveness of the proposed scheme in reducing the error between the estimated and the true dispersion model parameters compared to non-adaptive sensing strategies. In addition, the effect of using different optimality criteria, different numbers and types of UAVs as well as two options for decentralizing the waypoint calculation are investigated.

@INPROCEEDINGS{2017:ICRA-Euler, author = {J. Euler and O. von Stryk}, title = {Optimized Vehicle-Specific Trajectories for Cooperative Process Estimation by Sensor-Equipped UAVs}, year = {2017}, booktitle = {IEEE International Conference on Robotics and Automation (ICRA) (accepted)}, abstract = {This paper presents a sequential optimum design approach for estimating the parameters of an atmospheric dispersion process model based on measurement data gathered by a team of cooperating sensor-equipped UAVs. Locally optimal waypoint sequences that account for each UAV’s possibly heterogeneous motion dynamics are computed by minimizing a suitable optimality criterion. Following these waypoints, the UAVs cooperatively maximize the information gain of the acquired measurements. A decentralized data-driven online control scheme is proposed that couples parameter estimation, waypoint calculation, and vehicle control and enables the UAVs to adaptively observe the dynamic process and iteratively improve the parameter estimate. Simulations demonstrate the effectiveness of the proposed scheme in reducing the error between the estimated and the true dispersion model parameters compared to non-adaptive sensing strategies. In addition, the effect of using different optimality criteria, different numbers and types of UAVs as well as two options for decentralizing the waypoint calculation are investigated.},}

Juliane Euler

Optimal Cooperative Control of UAVs for Dynamic Data-Driven Monitoring Tasks

AbstractOnline state and parameter estimation of atmospheric dispersion processes using multiple mobile sensor platforms is a prominent example of Dynamic Data-Driven Application Systems (DDDAS). Based on repeated predictions of a partial differential equation (PDE) model and measurements of the sensor network, estimates are updated and sensor trajectories are adapted to obtain more informative measurements. While most of the monitoring strategies require a central supercomputer, a novel decentralized plume monitoring approach is proposed in this paper. It combines the benefits of distributed approaches like scalability and robustness with the prediction ability of PDE process models. The strategy comprises model order reduction to keep calculations computationally tractable and a joint Kalman Filter with Covariance Intersection for incorporating measurements and propagating state information in the sensor network. Moreover, a cooperative vehicle controller is employed to guide the sensor vehicles to dynamically updated target locations that are based on the current estimated error variance.

@ARTICLE{Ritter_Decentralized_2016, author = {Tobias Ritter and Juliane Euler and Stefan Ulbrich and Oskar von Stryk}, title = {Decentralized Dynamic Data-driven Monitoring of Atmospheric Dispersion Processes}, journal = {Procedia Computer Science}, year = {2016}, volume = {80}, pages = {919 - 930}, note = {International Conference on Computational Science 2016, {ICCS} 2016, 6-8 June 2016, San Diego, California, {USA}}, doi = {10.1016/j.procs.2016.05.382}, url = {http://www.sciencedirect.com/science/article/pii/S1877050916308584}, pdf = {2016_Ritter_ICCS.pdf}, abstract = {Online state and parameter estimation of atmospheric dispersion processes using multiple mobile sensor platforms is a prominent example of Dynamic Data-Driven Application Systems (DDDAS). Based on repeated predictions of a partial differential equation (PDE) model and measurements of the sensor network, estimates are updated and sensor trajectories are adapted to obtain more informative measurements. While most of the monitoring strategies require a central supercomputer, a novel decentralized plume monitoring approach is proposed in this paper. It combines the benefits of distributed approaches like scalability and robustness with the prediction ability of PDE process models. The strategy comprises model order reduction to keep calculations computationally tractable and a joint Kalman Filter with Covariance Intersection for incorporating measurements and propagating state information in the sensor network. Moreover, a cooperative vehicle controller is employed to guide the sensor vehicles to dynamically updated target locations that are based on the current estimated error variance.},}

AbstractOptimal coordination of multiple sensors is crucial for efficient atmospheric dispersion estimation. The proposed approach adaptively provides optimized trajectories with respect to sensor cooperation and uncertainty reduction of the process estimate. To avoid the time-consuming solution of a complex optimal control problem, estimation and vehicle control are considered separate problems linked in a sequential procedure. Based on a partial differential equation model, the Ensemble Transform Kalman Filter is applied for data assimilation and generation of observation targets offering maximum information gain. A centralized model-predictive vehicle controller simultaneously provides optimal target allocation and collision-free path planning. Extending previous work, continuous measuring is assumed, which attaches more significance to the course of the trajectories. Local attraction points are introduced to draw the sensors to regions of high uncertainty. Moreover, improved target updates increase the sampling efficiency. A simulated test case illustrates the approach in comparison to non-attracted trajectories.

AbstractEfficient online state estimation of dynamic dispersion processes plays an important role in a variety of safety-critical applications. The use of mobile sensor platforms is increasingly considered in this context, but implies the generation of situation-dependent vehicle trajectories providing high information gain in real-time. In this paper, a new adaptive observation strategy is presented combining state estimation based on partial differential equation models of the dispersion process with a model-predictive control approach for multiple cooperating mobile sensors. In a repeating sequential procedure, based on the Ensemble Transform Kalman Filter, the uncertainty of the current estimate is determined and used to find valuable measurement locations. Those serve as target points for the controller providing optimal trajectories subject to the vehicles’ motion dynamics and cooperation constraints. First promising results regarding accuracy and efficiency were obtained.

@INPROCEEDINGS{2014:Ritter-etal, author = {T. Ritter and J. Euler and S. Ulbrich and O. von Stryk}, title = {Adaptive Observation Strategy for Dispersion Process Estimation Using Cooperating Mobile Sensors}, year = {2014}, pages = {5302 - 5308}, month = {Aug 24 - 29}, address = {Cape Town, South Africa}, booktitle = {Proceedings of the 19th IFAC World Congress}, pdf = {2014_RitterEtAl_IFAC.pdf}, abstract = {Efficient online state estimation of dynamic dispersion processes plays an important role in a variety of safety-critical applications. The use of mobile sensor platforms is increasingly considered in this context, but implies the generation of situation-dependent vehicle trajectories providing high information gain in real-time. In this paper, a new adaptive observation strategy is presented combining state estimation based on partial differential equation models of the dispersion process with a model-predictive control approach for multiple cooperating mobile sensors. In a repeating sequential procedure, based on the Ensemble Transform Kalman Filter, the uncertainty of the current estimate is determined and used to find valuable measurement locations. Those serve as target points for the controller providing optimal trajectories subject to the vehicles’ motion dynamics and cooperation constraints. First promising results regarding accuracy and efficiency were obtained. },}

2013

J. Euler, O. von Stryk

Optimal Cooperative Control of Mobile Sensors for Dynamic Process Estimation

AbstractA typical mission for robotic systems in environ- mental monitoring is the identification of dynamic processes like atmospheric dispersion by a group of mobile sensors. Due to this problem’s large-scale and highly dynamic character, an efficient cooperative sampling strategy is required. This paper presents a mathematical concept for estimating the parameters of a Gaussian puff model of the dispersion process based on cooperatively gathered measurement data. The sensors’ cooperative behavior is determined by a distributed model-predictive controller. It combines task allocation and tra- jectory planning in a single mixed-integer problem formulation employing linearly approximated vehicle dynamics models. By integrating the quality of the dispersion model parameters as an additional optimization criterion for the controller, the pro- posed method intends to simultaneously solve both the estimation problem and the cooperative control problem in a near optimal manner.

@MISC{euler13, author = {J. Euler and O. von Stryk}, title = {Optimal Cooperative Control of Mobile Sensors for Dynamic Process Estimation}, year = {2013}, note = {Workshop on Robotics for Environmental Monitoring at Robotics: Science and Systems 2013, Jun 24 - 28}, abstract = {A typical mission for robotic systems in environ- mental monitoring is the identification of dynamic processes like atmospheric dispersion by a group of mobile sensors. Due to this problem’s large-scale and highly dynamic character, an efficient cooperative sampling strategy is required. This paper presents a mathematical concept for estimating the parameters of a Gaussian puff model of the dispersion process based on cooperatively gathered measurement data. The sensors’ cooperative behavior is determined by a distributed model-predictive controller. It combines task allocation and tra- jectory planning in a single mixed-integer problem formulation employing linearly approximated vehicle dynamics models. By integrating the quality of the dispersion model parameters as an additional optimization criterion for the controller, the pro- posed method intends to simultaneously solve both the estimation problem and the cooperative control problem in a near optimal manner. },}

2012

J. Euler, A. Horn, D. Haumann, J. Adamy, O. von Stryk

Cooperative N-Boundary Tracking in Large Scale Environments

In: Proceedings of the IEEE 9th International Conference on Mobile Adhoc and Sensor Systems (MASS), 2012

AbstractMonitoring in large scale environments is a typ-ical mission in cooperative robotics. This task requires the exploration of a huge domain by a generally small number of sensor equipped mobile robots. As time restrictions prohibit an exhaustive global search, a sampling strategy is required that allows an efficient spatial mapping of the environment. This paper proposes an adaptive sampling strategy for efficient simultaneous tracking of multiple concentration levels of an atmospheric plume by a team of cooperating unmanned aerial vehicles (UAVs). The approach combines uncertainty and correlation-based concentration estimates to generate sampling points based on already gathered data. The adaptive generation of sampling locations is coupled to a distributed model-predictive controller for planning optimal vehicle trajectories under collision and communication constraints. Simulation results demonstrate that connectivity of all involved vehicles can be maintained and an accurate reconstruction of the plume is obtained efficiently.

@INPROCEEDINGS{euler12, author = {J. Euler and A. Horn and D. Haumann and J. Adamy and O. von Stryk}, title = {Cooperative N-Boundary Tracking in Large Scale Environments}, year = {2012}, booktitle = {Proceedings of the IEEE 9th International Conference on Mobile Adhoc and Sensor Systems (MASS)}, keywords = {adaptive sampling,boundary tracking,cooperative control,large scale environments}, doi = {10.1109/MASS.2012.6708518}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6708518}, pdf = {2012_EulerEtAl_WiSARN.pdf}, abstract = {Monitoring in large scale environments is a typ-ical mission in cooperative robotics. This task requires the exploration of a huge domain by a generally small number of sensor equipped mobile robots. As time restrictions prohibit an exhaustive global search, a sampling strategy is required that allows an efficient spatial mapping of the environment. This paper proposes an adaptive sampling strategy for efficient simultaneous tracking of multiple concentration levels of an atmospheric plume by a team of cooperating unmanned aerial vehicles (UAVs). The approach combines uncertainty and correlation-based concentration estimates to generate sampling points based on already gathered data. The adaptive generation of sampling locations is coupled to a distributed model-predictive controller for planning optimal vehicle trajectories under collision and communication constraints. Simulation results demonstrate that connectivity of all involved vehicles can be maintained and an accurate reconstruction of the plume is obtained efficiently.},}

AbstractThis paper describes the hardware and software design and developments of the kidsize humanoid robot systems of the Darmstadt Dribblers in 2012. The robots are used as a vehicle for research in humanoid robotics and teams of cooperating, autonomous robots. The Humanoid League of RoboCup provides an ideal testbed for investigation of topics like stability, control and versatility of humanoid locomotion, behavior control of autonomous humanoid robots and robot teams with many degrees of freedom and many actuated joints, perception and world modeling based on very limited human-like, external sensing abilities as well as benchmarking of autonomous robot performance. The methodologies developed by the Darmstadt Dribblers to address reflex and cognitive control layers, image processing, perception, world modeling, behavior and motion control, robot simulation, monitoring, debugging and bio-inspired humanoid robot bodyware are briefly discussed.

@TECHREPORT{2012:dd_tdp, author = {J. Kuhn and S. Kohlbrecher and K. Petersen and D. Scholz and J. Wojtusch and O. von Stryk}, title = {Team Description for Humanoid KidSize League of RoboCup 2012 }, year = {2012}, institution = {Technische Universität Darmstadt}, pdf = {2012_tdp_hum.pdf}, abstract = {This paper describes the hardware and software design and developments of the kidsize humanoid robot systems of the Darmstadt Dribblers in 2012. The robots are used as a vehicle for research in humanoid robotics and teams of cooperating, autonomous robots. The Humanoid League of RoboCup provides an ideal testbed for investigation of topics like stability, control and versatility of humanoid locomotion, behavior control of autonomous humanoid robots and robot teams with many degrees of freedom and many actuated joints, perception and world modeling based on very limited human-like, external sensing abilities as well as benchmarking of autonomous robot performance. The methodologies developed by the Darmstadt Dribblers to address reflex and cognitive control layers, image processing, perception, world modeling, behavior and motion control, robot simulation, monitoring, debugging and bio-inspired humanoid robot bodyware are briefly discussed. },}

AbstractThis paper describes the hardware and software design and developments of the kidsize humanoid robot systems of the Darmstadt Dribblers in 2011. The robots are used as a vehicle for research in humanoid robotics and teams of cooperating, autonomous robots. The Humanoid League of RoboCup provides an ideal testbed for investigation of topics like stability, control and versatility of humanoid locomotion, behavior control of autonomous humanoid robots and robot teams with many degrees of freedom and many actuated joints, perception and world modeling based on very limited human-like, external sensing abilities as well as benchmarking of autonomous robot performance. The methodologies developed by the Darmstadt Dribblers to address reflex and cognitive control layers, image processing, perception, world modeling, behavior and motion control, robot simulation, monitoring and debugging are briefly discussed.

@TECHREPORT{2011:dd_tdp, author = {M. Friedmann and J. Kuhn and S. Kohlbrecher and K. Petersen and D. Scholz and D. Thomas and J. Wojtusch and O. von Stryk}, title = {Darmstadt Dribblers - Team Description for Humanoid KidSize League of RoboCup 2011}, year = {2011}, institution = {Technische Universität Darmstadt}, pdf = {2011_tdp_hum.pdf}, abstract = {This paper describes the hardware and software design and developments of the kidsize humanoid robot systems of the Darmstadt Dribblers in 2011. The robots are used as a vehicle for research in humanoid robotics and teams of cooperating, autonomous robots. The Humanoid League of RoboCup provides an ideal testbed for investigation of topics like stability, control and versatility of humanoid locomotion, behavior control of autonomous humanoid robots and robot teams with many degrees of freedom and many actuated joints, perception and world modeling based on very limited human-like, external sensing abilities as well as benchmarking of autonomous robot performance. The methodologies developed by the Darmstadt Dribblers to address reflex and cognitive control layers, image processing, perception, world modeling, behavior and motion control, robot simulation, monitoring and debugging are briefly discussed.},}

J. Kuhn, C. Reinl, O. von Stryk

Predictive Control for Multi-Robot Observation of Multiple Moving Targets Based on Discrete-Continuous Linear Models

AbstractThe observation of multiple moving targets by cooperating mobile robots is a key problem in many security, surveillance and service applications. In essence, this problem is characterized by a tight coupling of target allocation and continuous trajectory planning. Optimal control of the multi-robot system generally neither permits to neglect physical motion dynamics nor to decouple or successively process target assignment and trajectory planning. In this paper, a numerically robust and stable model-predictive control strategy for solving the problem in the case of discrete-time double-integrator dynamics is presented. Optimization based on linear mixed logical dynamical system models allows for a flexible weighting of different aspects and optimal control inputs for settings of moderate size can be computed in real-time. By simulating sets of randomly generated situations, one can determine a maximum problem size solvable in real-time in terms of the number of considered robots, targets, and length of the prediction horizon. Based on this information, a decentralized control approach is proposed.

@INPROCEEDINGS{2011:KuhnReinlVonStryk_IFAC_WC11, author = {J. Kuhn and C. Reinl and O. von Stryk}, title = {Predictive Control for Multi-Robot Observation of Multiple Moving Targets Based on Discrete-Continuous Linear Models}, year = {2011}, month = {Aug 28 - Sep 2}, address = {Milano, Italy}, booktitle = {Proceedings of the 18th IFAC World Congress}, pdf = {2011-IFAC-KuhnReinlVonStryk.pdf}, abstract = {The observation of multiple moving targets by cooperating mobile robots is a key problem in many security, surveillance and service applications. In essence, this problem is characterized by a tight coupling of target allocation and continuous trajectory planning. Optimal control of the multi-robot system generally neither permits to neglect physical motion dynamics nor to decouple or successively process target assignment and trajectory planning. In this paper, a numerically robust and stable model-predictive control strategy for solving the problem in the case of discrete-time double-integrator dynamics is presented. Optimization based on linear mixed logical dynamical system models allows for a flexible weighting of different aspects and optimal control inputs for settings of moderate size can be computed in real-time. By simulating sets of randomly generated situations, one can determine a maximum problem size solvable in real-time in terms of the number of considered robots, targets, and length of the prediction horizon. Based on this information, a decentralized control approach is proposed. },}

2009

Juliane Kuhn

Model-Predictive Control of Cooperative Multi-Vehicle Systems Based on Discrete-Time Linear Systems